Towards AI-powered global-scale species distribution models
Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.
Learning Objectives:
Understand the capabilities of current deep learning methods for species range estimation.
Recognise the limitations of these models in the context of current open challenges in this space
Speakers:
Oisin Mac Oadha
Researcher, University of Edinburgh
Moderators:
Sara Beery
Professor, Artificial Intelligence and Decision-Making, MIT EECS
AI for Good is identifying innovative AI applications, building skills and standards, and advancing partnerships to solve global challenges.
AI for Good is organized by ITU in partnership with over 40 UN Sister Agencies and co-convened with the Government of Switzerland.
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The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
Estimating the geographic range of a species from sparse observations is a challenging and important geospatial prediction problem. Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. This problem has a long history in ecology, but traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets which can include tens of millions of observations of hundreds of thousands of species in addition to multi-modal image and text data. In this talk, I will present recent work from my group on deep learning-based solutions for estimating species’ ranges from incomplete data. I will also discuss some of the open challenges that exist in this space.
Learning Objectives:
Understand the capabilities of current deep learning methods for species range estimation.
Recognise the limitations of these models in the context of current open challenges in this space
Speakers:
Oisin Mac Oadha
Researcher, University of Edinburgh
Moderators:
Sara Beery
Professor, Artificial Intelligence and Decision-Making, MIT EECS
AI for Good is identifying innovative AI applications, building skills and standards, and advancing partnerships to solve global challenges.
AI for Good is organized by ITU in partnership with over 40 UN Sister Agencies and co-convened with the Government of Switzerland.
Join the Neural Network!
👉https://aiforgood.itu.int/neural-network/
The AI for Good networking community platform powered by AI.
Designed to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.
🔴 Watch the latest #AIforGood videos!
https://www.youtube.com/c/AIforGood/videos
📩 Stay updated and join our weekly AI for Good newsletter:
http://eepurl.com/gI2kJ5
🗞Check out the latest AI for Good news:
https://aiforgood.itu.int/newsroom/
📱Explore the AI for Good blog:
https://aiforgood.itu.int/ai-for-good-blog/
🌎 Connect on our social media:
Website: https://aiforgood.itu.int/
X: https://twitter.com/AIforGood
LinkedIn Page: https://www.linkedin.com/company/26511907
LinkedIn Group: https://www.linkedin.com/groups/8567748
Instagram: https://www.instagram.com/aiforgood
Facebook: https://www.facebook.com/AIforGood
Disclaimer:
The views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.
